Abstract: A robot may include a support portion and a conveyor. The conveyor may be coupled to the support portion. The conveyor may include a belt configured to interface with an object to cause the object to be positioned on or positioned off the belt. The conveyor may be configured to move along a height of the support portion to adjust a height of the belt relative to the support portion to permit the object to be positioned on or positioned off the belt at a variety of heights.

Abstract: A robot may include a body, a control system, and a control interface. The control system may be configured to cause the robot to transition between an autonomous mode and an intervention mode. In the autonomous mode, the control system may be configured to autonomously control the body within an environment, In the intervention mode, the control system may be configured to control the body within the environment based on operator input. The control interface may be configured to receive the operator input.

Abstract: A method may include obtaining an artificial intelligence (AI) model configured to identify series of tasks to be performed by a robot in accordance with general parameters. The method may include obtaining data indicating a particular parameter corresponding to a particular environment. The method may include identifying, using the AI model, the particular parameter as corresponding to the particular environment. The particular parameter may be used by the AI model to identify the series of tasks to be performed by the robot such that the series of tasks are performed in accordance with the general and the particular parameters. The method may include identifying, using the AI model and the particular parameter, a series of tasks to be performed by the robot to complete an operation in the particular environment. The method may include causing the robot to autonomously perform the series of tasks in the particular environment.

Abstract: A method may include receiving input data identifying an operation to be completed in an environment. The method may also include identifying, using an artificial intelligence (AI) model, a series of tasks to be performed by robots to complete the operation based on the input data. In addition, the method may include identifying a subset of the robots to perform the series of tasks based on capabilities to be used to perform the series of tasks. Further, the method may include causing the subset of the robots to autonomously perform the series of tasks to complete the operation.

Abstract: Ground detection of a touch sensitive device is disclosed. The device can detect its grounded state so that poor grounding can be selectively compensated for in touch signals outputted by the device. The device can include one or more components to monitor certain conditions of the device. The device can analyze the monitored conditions to determine the grounding condition of the device. The device can apply a function to compensate its touch signal outputs if the device determines that it is poorly grounded. Conversely, the device can omit the function if the device determines that it is well grounded.

Abstract: A system and method is provided for the control of a network of devices wherein each device of the networked devices provides for the operation of a sensor such as an accelerometer, processor and communication element within each device, and network and/or cloud based processing and storage, to process collected data to permit detection and predictive analysis of traffic patterns, weather patterns and other forces of nature. The system and method can analyze duration and magnitude of vibration signals, and considering maps and known locations of devices, tracks and highways and historical data regarding each, use machine learning techniques to accurately classify the motion and provide real-time and predictive analysis.

Abstract: A system and method for automatic path light control based on a detected size and classification of motion around the device using passive infrared (PIR) sensor technologies and distributed classification algorithms, and on detected light levels in and around the path area using ambient light sensor (ALS) technologies. By using such sensor data, the path light does not need to be maintained at a fixed value, which may be inadequate or inefficient at times, nor require constant user adjustments. Implementations of the disclosed subject matter enable automatic path light control that can be dynamic and automatically adjusted to fit the environment, the current user characteristics and the current user movements through the environment.

Abstract: Ground detection of a touch sensitive device is disclosed. The device can detect its grounded state so that poor grounding can be selectively compensated for in touch signals outputted by the device. The device can include one or more components to monitor certain conditions of the device. The device can analyze the monitored conditions to determine the grounding condition of the device. The device can apply a function to compensate its touch signal outputs if the device determines that it is poorly grounded. Conversely, the device can omit the function if the device determines that it is well grounded.

Abstract: Ground detection of a touch sensitive device is disclosed. The device can detect its grounded state so that poor grounding can be selectively compensated for in touch signals outputted by the device. The device can include one or more components to monitor certain conditions of the device. The device can analyze the monitored conditions to determine the grounding condition of the device. The device can apply a function to compensate its touch signal outputs if the device determines that it is poorly grounded. Conversely, the device can omit the function if the device determines that it is well grounded.

Abstract: A system and method is provided for the control of a network of devices wherein each device of the networked devices provides for the operation of a sensor such as an accelerometer, processor and communication element within each device, and network and/or cloud based processing and storage, to process collected data to permit detection and predictive analysis of traffic patterns, weather patterns and other forces of nature. The system and method can analyze duration and magnitude of vibration signals, and considering maps and known locations of devices, tracks and highways and historical data regarding each, use machine learning techniques to accurately classify the motion and provide real-time and predictive analysis.

Abstract: A system and method for automatic path light control based on a detected size and classification of motion around the device using passive infrared (PIR) sensor technologies and distributed classification algorithms, and on detected light levels in and around the path area using ambient light sensor (ALS) technologies. By using such sensor data, the path light does not need to be maintained at a fixed value, which may be inadequate or inefficient at times, nor require constant user adjustments. Implementations of the disclosed subject matter enable automatic path light control that can be dynamic and automatically adjusted to fit the environment, the current user characteristics and the current user movements through the environment.

Abstract: Ground detection of a touch sensitive device is disclosed. The device can detect its grounded state so that poor grounding can be selectively compensated for in touch signals outputted by the device. The device can include one or more components to monitor certain conditions of the device. The device can analyze the monitored conditions to determine the grounding condition of the device. The device can apply a function to compensate its touch signal outputs if the device determines that it is poorly grounded. Conversely, the device can omit the function if the device determines that it is well grounded.